class_docstr: A dataset that allows for active learning.
  
  Args:
      dataset: The baseline dataset.
      labelled: An array that acts as a mask which is greater than 1 for every
          data point that is labelled, and 0 for every data point that is not
          labelled.
      make_unlabelled: The function that returns an
          unlabelled version of a datum so that it can still be used in the DataLoader.
      random_state: Set the random seed for label_randomly().
      pool_specifics: Attributes to set when creating the pool.
                                       Useful to remove data augmentation.
      last_active_steps: If specified, will iterate over the last active steps
                          instead of the full dataset. Useful when doing partial finetuning.
docstr: Load the labelled map and random_state with give state_dict.
docstr_tok: ["Load","the","labelled","map","and","random_state","with","give","state_dict","."]
code: def load_state_dict(self, state_dict):
      """Load the labelled map and random_state with give state_dict."""
      self.labelled_map = state_dict["labelled"]
      self.random_state = state_dict["random_state"]
  
imports: import warnings
  from copy import deepcopy
  from itertools import zip_longest
  from typing import Union, Optional, Callable, Any, Dict, List, Sequence, Mapping
  import numpy
  import torch
  import torch.utils.data
  from torch import Tensor
  from baal.active.dataset.base import SplittedDataset, Dataset
  from baal.utils.equality import deep_check
label_desc: Use image node_id 10 for calling the ActiveLearningDataset obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types